種子質(zhì)量與安全檢驗的高光譜成像研究進(jìn)展

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種子質(zhì)量是植物育種和生產(chǎn)中的一個基礎(chǔ)性和關(guān)鍵性因素，可以通過種子的發(fā)芽率或理化特性來衡量，在農(nóng)業(yè)領(lǐng)域已變得越來越重要。一方面，優(yōu)質(zhì)種子是植物生長的良好開端，預(yù)示著豐收；另一方面，種子質(zhì)量通常與食品質(zhì)量密切相關(guān)，如質(zhì)地、風(fēng)味和營養(yǎng)成分。為了滿足消費者的需求，種子在收獲后應(yīng)謹(jǐn)慎加工和儲存。在采收、加工和儲存過程中，需要一種快速、準(zhǔn)確、無損的檢測種子質(zhì)量的方法。高光譜成像作為一種非破壞性、快速的種子質(zhì)量和安全性評價方法，近年來備受關(guān)注。

高光譜成像技術(shù)結(jié)合了光譜技術(shù)和成像技術(shù)的優(yōu)點，可以同時獲取光譜和空間信息。也就是說，它可以同時獲得不均勻樣品的化學(xué)信息和化學(xué)成分的空間分布。高光譜技術(shù)在農(nóng)業(yè)、食品、醫(yī)藥等行業(yè)得到了廣泛的應(yīng)用。高光譜成像技術(shù)在種子行業(yè)的潛在或?qū)嶋H應(yīng)用包括種子活性、活力、缺陷、疾病、凈度檢測，種子成分測定。

本文總結(jié)和分析了高光譜技術(shù)在種子質(zhì)量和安全檢驗方面的發(fā)展，介紹了該技術(shù)在種子分類分級、活性和活力檢測、損傷（缺陷和真菌）檢測、凈度檢測和種子成分測定等方面的能力，綜述了該技術(shù)在種子質(zhì)量檢測和安全檢測中的應(yīng)用，包括分析的光譜范圍、樣品種類、樣品狀態(tài)、樣品數(shù)量、特征（光譜特征、圖像特征、特征提取方法）、信號模式等。

表1高光譜成像應(yīng)用于種子分類和分級的參考文獻(xiàn)摘要

Seed	Varieties	Features		Data analysis strategies		Main application type	Classification result (highest accuracy)
Seed	Varieties	Spectra/image	Extraction/selection methods	Analysis level	Classification/regression methods	Main application type	Classification result (highest accuracy)
Barley, wheat and sorghum	1 variety of each kind of grain	Spectra	PCA	PWbprediction map and OWc(single kernels)	–	Grain topography classification	–
Black bean	3	Spectra and image	SPA, PCA, GLCM	OW (single kernels)	PLS-DA, SVM	Variety classification	98.33% (PLS-DA)
Grape seed	3 varieties, two growth soil	Spectra	PCA	OW (single kernels), PW PCA and prediction map	GDA	Assess Stage of maturation of grape seeds	> 95%
Grape seed	3	Spectra and image	PCA	OW (single kernels)	SVM	Variety classification	94.30%
Maize	2 (transgenic and non-transgenic)	Spectra	PCA, CARS	PW PCA and prediction map, OW (single kernels)	PLS-DA, SVM	Transgenic and non-transgenic classification	99.5% (PLS-DA)
Maize	4 varieties, 3 crop years	Spectra	no	OW (single kernels)	LS-SVM	Variety classification	91.50%
Maize	4 varieties, 3 crop years	Spectra	no	OW (single kernels)	LS-SVM	Variety classification	94.80%
Maize	4 varieties, 3 crop years	Spectra	no	OW (single kernels)	LS-SVM	Variety classification	94.40%
Maize	17	Spectra and image	PCA, SPA, GLCM, MDS	OW (single kernels)	LS-SVM	Variety classification	94.40%
Maize	18	Spectra and image	PCA	OW (single kernels), PW PCA and prediction map	PLS-DA	Textural, vitreous, floury and the third type endosperm	85% (PLS-DA)
Maize	3 hardness	Spectra and image	PCA	PW PCA and prediction map, OW (single kernels)	PLS-DA	Hardness classification	97% (PLS-DA)
Maize	14	Spectra	joint skewness-based wavelength selection	OW (single kernels)	LS-SVM	Variety classification	98.18%
Maize	3	Spectra and image	PCA	OW (single kernels)	SVM, RBFNN	Variety classification	93.85% (RBFNN)
Maize	6	Spectra and image	PCA, KPCA, GLCM	OW (bulk samples)	LS-SVM, BPNN, PCA, KPCs	Classes classification	98.89% (PCA-GLCM-LS-SVM)
Rice	4 origins	Spectra and image	PCA, GLCM	OW (single kernels)	SVM	Variety classification	91.67%
Rice	4	Spectra	PLS-DA, PCA	PW PCA and OW (bulk samples)	KNN, PLS-DA, SIMCA, SVM, RF	Seed cultivars classification	100% (SIMCA, SVM, and RF)
Soybean, maize and rice	3 of each kind of seed	Spectra	neighborhood mutual information	OW (single kernels)	ELM, RF	Variety classification	100% (ELM)
Waxy corn	4	Spectra and image	SPA, GLCM	OW (single kernels)	PLS-DA, SVM	Variety classification	98.2% (SVM)
Wheat	8	Image	WT, STEPDISC, PCA	PW and OW (bulk samples)	BPNN, LDA, QDA	Classes classification	99.1% (LDA)
Wheat	8	Spectra	STEPDISC	OW (bulk samples)	LDA, QDA, Standard BPNN, Wardnet BPNN	Variety classification	94–100% (LDA)
Wheat	5	Spectra	STEPDISC	PW PCA and OW (bulk samples)	LDA, QDA	Classes classification	90–100% (LDA)

表2 高光譜成像應(yīng)用于種子活力和活力檢測的參考文獻(xiàn)摘要

Seed	Varieties	Features		Data analysis strategies		Main application type	Classification result (highest accuracy)
Seed	Varieties	Spectra/image	Extraction/selection methods	Analysis level	Classification/regression methods	Main application type	Classification result (highest accuracy)
Barley	1 variety, 8 treatments	Spectra	PCA, MNF	PWbprediction map and OWc(single kernels)	Maximum likelihood multinomial, regression classifier	Germination level detection	97% when single kernels grouped into the three categories
Corn	3 varieties, 2 treatments	Spectra	No	OW (single kernels)	PLS-DA	Viability prediction	> 95.6%
Cryptomeria japonica and Chamaecyparis obtuse	2 treatments of each kind of seed	Spectra	No	OW (single kernels)	Spectral index	Viability prediction	98.30%
Cucumber	1 variety, 2 treatments	Spectra	No	OW (single kernels), PW prediction map	PLS-DA	Viability prediction	100%
Muskmelon	1 variety, 4 treatments	Spectra	VIP, SR, and SMC	OW (single kernels)	PLS-DA	Viability prediction	94.60%
Norway spruce	1 variety, 3 treatments	Spectra and image	L1-regularized logistic regression based feature selection	OW (single kernels)	SVM	Viability prediction	> 93%
Pepper	1 variety, 2 treatments	Spectra	No	OW (single kernels), PW prediction map	PLS-DA	Germination level detection	> 85%
Tree seeds	3 varieties, 8 treatments	Spectra	LDA	OW (single kernels)	LDA	Germination level detection	> 79%
Wheat, barley and sorghum	B: 3 varieties W: 3 varieties S: 2, varieties 6 treatments	Spectra	PCA	OW (single kernels), PW prediction map	PLS-DA, PLSR	Viability prediction	R = 0.92 (PLS-DA)

表3 高光譜成像應(yīng)用于種子質(zhì)量缺陷檢測的參考文獻(xiàn)摘要

Seed	Varieties	Features		Data analysis strategies		Main application type	Classification result (highest accuracy)
Seed	Varieties	Spectra/image	Extraction/selection methods	Analysis level	Classification/regression methods	Main application type	Classification result (highest accuracy)
Mung bean	1 variety, 8 treatments	Spectra and image	PCA	OWb(single kernels)	LDA, QDA	Insect damage detection	> 82%
Soybean	1 variety, 5 treatments	Spectra and image	GLCM	OW (single kernels)	LDA, QDA	Insect damage detection	99% (QDA)
Wheat	1 variety, 4 insect varieties	Spectra and image	STEPDISC, GLCM, GLRM, PCA	OW (single kernels)	LDA, QDA	Insect damage detection	95.3–99.3%
Wheat	1 variety, 3 treatments	Spectra and image	PCA	PWcprediction map and OW (single kernels)	Spectral index	Seed sprouted detection	> 90%

表4 高光譜成像應(yīng)用于種子真菌損傷檢測的參考文獻(xiàn)摘要

Seed	Varieties	Features		Data analysis strategies		Main application type	Classification result (highest accuracy)
Seed	Varieties	Spectra/image	Extraction/selection methods	Analysis level	Classification/regression methods	Main application type	Classification result (highest accuracy)
Barley	1 variety, 2 fungi	Spectra and image	PCA	PWbprediction map and OWc(single kernels)	LDA, QDA, MDA	Fungus (Ochratoxin A and Penicillium) damage detection	> 82%
Canola	1 variety, 2 fungi,	Spectra and image	PCA	OW (single kernels)	LDA, QDA, MDA	Fungus (Aspergillus glaucus and Penicilliumspp.) damage detection	> 90%
Corn	3 varieties, 5 treatments	Spectra	No	OW (single kernels), PW prediction map	PLS-DA	Fungus (Aflatoxin B1) damage detection	96.90%
Corn	1 variety, 3 treatments	Spectra	No	PW spectra	spectral index	Fungus (Aflatoxin A. flavus) damage detection	93%
Corn	1 variety, 3 treatments	Spectra	PCA	OW (single kernels), PW PCA	LS-SVM, KNN	Fungus (Aflatoxin A. flavus) damage detection	> 91% (KNN)
Hick peas, green peas, lentils, pinto beans and kidney beans	5 different pulses, 2 fungi	Spectra and image	PCA	OW (single kernels), PW PCA	LDA, QDA	Fungus (Penicillium commune Thom, C. and A. flavus Link, J.) damage detection	96%-100%
Maize	4 varieties	Spectra	PCA	OW (single kernels), PW prediction map	SVM, SVR	Fungus (Aflatoxin B1) damage detection	R2 = 0.77
Maize	1 variety, 5 treatments	Spectra	PCA, FDA	OW (single kernels), PW PCA	FDA	Fungus (Aflatoxin B1) damage detection	88%
Maize	1 variety, 5 treatments	Spectra	PCA	OW (single kernels)	FDA	Fungus (Aflatoxin B1) damage detection	98%
Maize	1 variety, 3 treatments	Spectra	No	OW (single kernels), PW prediction map	PLS-DA	Fungus (Fusarium) damage detection	77% (PLS-DA)
Maize	1 variety, nine treatments	Spectra	PCA, variable importance plots	OW (single kernels), PW PCA and prediction map	PLSR	Fungus damage detection	R2 = 0.87
maize	1 variety, 2 fungi, 3 treatments	Spectra	No	OW (single kernels)	discriminant analysis	Fungus (Toxigenic and atoxigenic A. flavus) damage detection	94.40%
Maize	12 varieties, 4 fungi	Spectra	PCA	OW (bulk samples), PW PCA	ANOVA, Fisher’s LSD test	Fungus (Aspergillus strains) damage detection	Fisher’s LSD test
Oat50	1 variety, 4 treatments	Spectra	PLSR	OW (single kernels), PW prediction map	PLSR, PLS-LDA	Fungus (Fusarium) damage detection	R2 = 0.8
Peanut	1 variety, 2 treatments	Spectra	PCA	OW (single kernels), PW prediction map	PCA	Moldy kernel detection	98.73%
Peanut	1 variety, 2 treatments	Spectra	ANOVA, NWFE	OW (single kernels), PW prediction map	SVM	Fungus (Aflatoxin) damage detection	> 94%
Rice	1 variety, 6 treatments	Spectra	No	OW (bulk samples)	SOM, PLSR	Fungus (Aspergillus) damage detection	R2 = 0.97
Watermelon	1 variety, 2 treatments	Spectra	Intermediate PLS (iPLS)	OW (single kernels) PW prediction map	PLS-DA, LS-SVM	Fungus (Cucumber green mottle mosaic virus) damage detection	83.3% (LS-SVM)
Watermelon	1 variety, 2 treatments	Spectra	Intermediate PLS (iPLS)	OW (single kernels), PW prediction map	PLS-DA, LS-SVM	Fungus (Acidovorax citrulli) damage detection	> 90%
Wheat	4 varieties, 2 fungi	Spectra	PCA	OW (single kernels), PW spectra	LDA	Fungus (Fusarium) damage detection	> 91%
Wheat	33 varieties, 3 treatments	Spectra	No	OW (single kernels), PW spectra	spectral index	Fungus (Fusarium head blight) damage detection	81%
Wheat	1 variety, 3 treatments	Spectra and image	PCA, STEPDISC	OW (single kernels)	LDA	Fungus (Fusarium) damage detection	92%
Wheat	1 variety, 3 fungi	Spectra and image	STEPDISC, GLCM, GLRM, PCA	OW (single kernels)	LDA, QDA, MDA	Fungus (Penicilliumspp., Aspergillus glaucus group, and Aspergillus niger) damage detection	> 95%
Wheat	3 varieties	Spectra	PCA	OW (bulk, single kernels), PW PCA	PLS-DA, iPLS-DA	Fungus (Fusarium) damage detection	99%

高光譜成像是一個復(fù)雜的、多學(xué)科的領(lǐng)域，其目的是在不進(jìn)行單調(diào)的樣品制備情況下，同時對多種化學(xué)成分和物理屬性的含量和空間分布進(jìn)行有效和可靠的測量，因此為種子自動分級和缺陷檢測系統(tǒng)的設(shè)計提供了可能。本文概述的各種應(yīng)用表明，在種子分級、活力和活力檢測、缺陷和疾病檢測、清潔度檢測和種子成分測定方面，高光譜成像具有很大的應(yīng)用潛力�？梢灶A(yù)見，采用該技術(shù)的實時種子監(jiān)測系統(tǒng)將在不久的將來滿足現(xiàn)代種子工業(yè)控制和分選系統(tǒng)的需求。

全文閱讀

Feng L, Zhu S, Liu F, et al, et al. Hyperspectral imaging for seed quality and safety inspection: a review. Plant Methods, 2019, 15(1): 1-25.

索取資料

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種子質(zhì)量與安全檢驗的高光譜成像研究進(jìn)展